reg_loss = tf.reduce_mean([tf.reduce_mean(reg_loss)
for reg_loss in reg_losses])
loss = loss + reg_loss
tf.summary.scalar("loss",loss,[engine.logging.CONSOLE,engine.logging.LOG])
////////////////////////////////////
// This should probably be refactored into an application class
// Averages are in name_scope for Tensorboard naming; summaries are outside for console naming
After Change
// This should probably be refactored into an application class
// Averages are in name_scope for Tensorboard naming; summaries are outside for console naming
with tf.name_scope("ConsoleLogging"):
logs=[["loss",loss]]
if param.application_type == "segmentation":
// TODO compute miss for dfferent target types
logs.append(["miss", tf.reduce_mean(tf.cast(
tf.not_equal(tf.argmax(predictions, -1), labels[..., 0]),
